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SUMMARY:第56回NITEP談話会
DTSTART;VALUE=DATE-TIME:20250725T041500Z
DTEND;VALUE=DATE-TIME:20250725T054500Z
DTSTAMP;VALUE=DATE-TIME:20260713T124054Z
UID:indico-event-199@indico.nitep.omu.ac.jp
DESCRIPTION:Machine-learning enhanced quantum state tomography and its app
 lications to the gravitational wave detectors\n\nRay-Kuang Lee\, National 
 Tsing Hua University\, Taiwan\n\nDate: 2025/7/25 Friday\, 13:15-14:45\n\nP
 lace:E101 room (理学部　E101号室)\n\nAbstract\n\nWith this talk\, I 
 will first illustrate the implementation of our machine-learning (ML) enha
 nced quantum state tomography (QST) for continuous variables\, through the
  experimentally measured data generated from squeezed vacuum states\, as a
 n example of quantum machine learning. Our recent progress in applying suc
 h a ML-QST as a crucial diagnostic toolbox for applications with squeezed 
 states\, from Wigner currents\, optical cat state generation\, and Bayesia
 n estimation for gravitational wave detectors will be reported.\n\n[1] Hsi
 en-Yi Hsieh\, et al.\, "Extract the Degradation Information in Squeezed St
 ates with Machine Learning\," Phys. Rev. Lett. 128\,  073604 (2022).\n[2]
  Hsien-Yi Hsieh\, et al.\, "Neural-network-enhanced Fock-state tomography\
 ," Phys. Rev. A 110\, 053705 (2024).\n[3] Yi-Ru Chen\, et al.\, "Generatio
 n of heralded optical cat states by photon addition\," Phys. Rev. A 110\, 
 023703 (2024)\n[4] Yi-Ru Chen\, et al.\, "Experimental reconstruction of W
 igner phase-space current\," Phys. Rev. A 108\, 023729 (2023).\n[5] Hsun-C
 hung Wu\, et al.\, "Machine learning enhanced quantum state tomography on 
 a field-programmable gate array\," APL Quantum 2\, 026117 (2025)\; Cover\;
  Featured Article\;  Scilight.\n[6] Ole Steuernagel and RKL\, "Quantumnes
 s Measure from Phase Space Distributions\," [arXiv: 2311.17399].\n\nhttps:
 //indico.nitep.omu.ac.jp/event/199/
LOCATION:
URL:https://indico.nitep.omu.ac.jp/event/199/
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